dynasent.py

# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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# Lint as: python3


"""Dynabench.DynaSent"""
from __future__ import absolute_import, division, print_function
import json
import os
from collections import OrderedDict
import datasets
logger = datasets.logging.get_logger(__name__)
_VERSION = datasets.Version("1.1.0") # v1.1 fixed for example uid.
_NUM_ROUNDS = 2
_DESCRIPTION = """\
    Dynabench.DynaSent is a Sentiment Analysis dataset collected using a 
    human-and-model-in-the-loop.
""".strip()


class DynabenchRoundDetails:
    """Round details for Dynabench.DynaSent datasets."""
    def __init__(
        self, citation, description, homepage, data_license, data_url, 
        data_features, data_subset_map=None
    ):
        self.citation = citation
        self.description = description
        self.homepage = homepage
        self.data_license = data_license
        self.data_url = data_url
        self.data_features = data_features
        self.data_subset_map = data_subset_map
# Provide the details for each round
_ROUND_DETAILS = {
    1: DynabenchRoundDetails(
        citation="""\
            @article{
                potts-etal-2020-dynasent,
                title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},
                author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus
                        and Kiela, Douwe},
                journal={arXiv preprint arXiv:2012.15349},
                url={https://arxiv.org/abs/2012.15349},
                year={2020}
            }
            """.strip(),
        description="""\
            DynaSent is an English-language benchmark task for ternary
            (positive/negative/neutral) sentiment analysis.
            For more details on the dataset construction process,
            see https://github.com/cgpotts/dynasent.
            """.strip(),
        homepage="https://dynabench.org/tasks/3",
        data_license="CC BY 4.0",
        data_url="https://github.com/cgpotts/dynasent/raw/main/dynasent-v1.1.zip",
        data_features=datasets.Features(
            {
                "id": datasets.Value("string"),
                "hit_ids": datasets.features.Sequence(
                    datasets.Value("string")
                ),
                "sentence": datasets.Value("string"),
                "indices_into_review_text": datasets.features.Sequence(
                    datasets.Value("int32")
                ),
                "model_0_label": datasets.Value("string"),
                "model_0_probs": {
                        "negative": datasets.Value("float32"),
                        "positive": datasets.Value("float32"),
                        "neutral": datasets.Value("float32")
                },
                "text_id": datasets.Value("string"),
                "review_id": datasets.Value("string"),
                "review_rating": datasets.Value("int32"),
                "label_distribution": {
                    "positive": datasets.features.Sequence(
                        datasets.Value("string")
                    ),
                    "negative": datasets.features.Sequence(
                        datasets.Value("string")
                    ),
                    "neutral": datasets.features.Sequence(
                        datasets.Value("string")
                    ),
                    "mixed": datasets.features.Sequence(
                        datasets.Value("string")
                    )
                },
                "gold_label": datasets.Value("string"),
                "metadata": {
                    "split": datasets.Value("string"),
                    "round": datasets.Value("int32"),
                    "subset": datasets.Value("string"),
                    "model_in_the_loop": datasets.Value("string"),
                }
            }
        ),
        data_subset_map=OrderedDict({
            "all": {
                "dir": "dynasent-v1.1",
                "file_prefix": "dynasent-v1.1-round01-yelp-", 
                "model": "RoBERTa"
            }
        }),
    ),
    2: DynabenchRoundDetails(
        citation="""\
            @article{
                potts-etal-2020-dynasent,
                title={{DynaSent}: A Dynamic Benchmark for Sentiment Analysis},
                author={Potts, Christopher and Wu, Zhengxuan and Geiger, Atticus
                        and Kiela, Douwe},
                journal={arXiv preprint arXiv:2012.15349},
                url={https://arxiv.org/abs/2012.15349},
                year={2020}
            }
            """.strip(),
        description="""\
            DynaSent is an English-language benchmark task for ternary
            (positive/negative/neutral) sentiment analysis.
            For more details on the dataset construction process,
            see https://github.com/cgpotts/dynasent.
            """.strip(),
        homepage="https://dynabench.org/tasks/3",
        data_license="CC BY 4.0",
        data_url="https://github.com/cgpotts/dynasent/raw/main/dynasent-v1.1.zip",
        data_features=datasets.Features(
            {
                "id": datasets.Value("string"),
                "hit_ids": datasets.features.Sequence(
                    datasets.Value("string")
                ),
                "sentence": datasets.Value("string"),
                "sentence_author": datasets.Value("string"),
                "has_prompt": datasets.Value("bool"),
                "prompt_data": {
                    "indices_into_review_text": datasets.features.Sequence(
                        datasets.Value("int32")
                    ),
                    "review_rating": datasets.Value("int32"), 
                    "prompt_sentence": datasets.Value("string"), 
                    "review_id": datasets.Value("string")
                },
                "model_1_label": datasets.Value("string"),
                "model_1_probs": {
                        "negative": datasets.Value("float32"),
                        "positive": datasets.Value("float32"),
                        "neutral": datasets.Value("float32")
                },
                "text_id": datasets.Value("string"),
                "label_distribution": {
                    "positive": datasets.features.Sequence(
                        datasets.Value("string")
                    ),
                    "negative": datasets.features.Sequence(
                        datasets.Value("string")
                    ),
                    "neutral": datasets.features.Sequence(
                        datasets.Value("string")
                    ),
                    "mixed": datasets.features.Sequence(
                        datasets.Value("string")
                    )
                },
                "gold_label": datasets.Value("string"),
                "metadata": {
                    "split": datasets.Value("string"),
                    "round": datasets.Value("int32"),
                    "subset": datasets.Value("string"),
                    "model_in_the_loop": datasets.Value("string"),
                }
            }
        ),
        data_subset_map=OrderedDict({
            "all": {
                "dir": "dynasent-v1.1",
                "file_prefix": "dynasent-v1.1-round02-dynabench-", 
                "model": "RoBERTa"
            }
        }),
    )
}


class DynabenchDynaSentConfig(datasets.BuilderConfig):
    """BuilderConfig for Dynabench.DynaSent datasets."""
    def __init__(self, round, subset='all', **kwargs):
        """BuilderConfig for Dynabench.DynaSent.
        Args:
          round: integer, the dynabench round to load.
          subset: string, the subset of that round's data to load or 'all'.
          **kwargs: keyword arguments forwarded to super.
        """
        assert isinstance(round, int), "round ({}) must be set and of type integer".format(round)
        assert 0 < round <= _NUM_ROUNDS, \
            "round (received {}) must be between 1 and {}".format(round, _NUM_ROUNDS)
        super(DynabenchDynaSentConfig, self).__init__(
            name="dynabench.dynasent.r{}.{}".format(round, subset),
            description="Dynabench DynaSent dataset for round {}, showing dataset selection: {}.".format(round, subset),
            **kwargs,
        )
        self.round = round
        self.subset = subset


class DynabenchDynaSent(datasets.GeneratorBasedBuilder):
    """Dynabench.DynaSent"""
    BUILDER_CONFIG_CLASS = DynabenchDynaSentConfig
    BUILDER_CONFIGS = [
        DynabenchDynaSentConfig(
            version=_VERSION,
            round=round,
            subset=subset,
        )  # pylint:disable=g-complex-comprehension
        for round in range(1, _NUM_ROUNDS+1) for subset in _ROUND_DETAILS[round].data_subset_map
    ]

    def _info(self):
        round_details = _ROUND_DETAILS[self.config.round]
        return datasets.DatasetInfo(
            description=round_details.description,
            features=round_details.data_features,
            homepage=round_details.homepage,
            citation=round_details.citation,
            supervised_keys=None
        )

    @staticmethod
    def _get_filepath(dl_dir, round, subset, split):
        round_details = _ROUND_DETAILS[round]
        return os.path.join(
            dl_dir,
            round_details.data_subset_map[subset]["dir"],
            round_details.data_subset_map[subset]["file_prefix"] + split + ".jsonl"
        )

    def _split_generators(self, dl_manager):
        round_details = _ROUND_DETAILS[self.config.round]
        dl_dir = dl_manager.download_and_extract(round_details.data_url)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": self._get_filepath(
                        dl_dir, self.config.round, self.config.subset, "train"
                    ),
                    "split": "train",
                    "round": self.config.round,
                    "subset": self.config.subset,
                    "model_in_the_loop": round_details.data_subset_map[self.config.subset]["model"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": self._get_filepath(
                        dl_dir, self.config.round, self.config.subset, "dev"
                    ),
                    "split": "validation",
                    "round": self.config.round,
                    "subset": self.config.subset,
                    "model_in_the_loop": round_details.data_subset_map[self.config.subset]["model"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": self._get_filepath(
                        dl_dir, self.config.round, self.config.subset, "test"
                    ),
                    "split": "test",
                    "round": self.config.round,
                    "subset": self.config.subset,
                    "model_in_the_loop": round_details.data_subset_map[self.config.subset]["model"],
                },
            ),
        ]

    def _generate_examples(self, filepath, split, round, subset, model_in_the_loop):
        """This function returns the examples in the raw (text) form."""
        ternary_labels = ('positive', 'negative', 'neutral') # Enforce to be the tenary version now.
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            for line in f:
                d = json.loads(line)
                if d['gold_label'] in ternary_labels:
                    if round == 1:
                        # Construct DynaSent features.
                        yield d["text_id"], {
                            "id": d["text_id"],
                            # DynaSent Example.
                            "hit_ids": d["hit_ids"],
                            "sentence": d["sentence"],
                            "indices_into_review_text": d["indices_into_review_text"],
                            "model_0_label": d["model_0_label"],
                            "model_0_probs": d["model_0_probs"],
                            "text_id": d["text_id"],
                            "review_id": d["review_id"],
                            "review_rating": d["review_rating"],
                            "label_distribution": d["label_distribution"],
                            "gold_label": d["gold_label"],
                            # Metadata.
                            "metadata": {
                                "split": split,
                                "round": round,
                                "subset": subset,
                                "model_in_the_loop": model_in_the_loop
                            }
                        }
                    elif round == 2:
                        # Construct DynaSent features.
                        if d["has_prompt"]:
                            if "indices_into_review_text" in d["prompt_data"]:
                                indices_into_review_text = d["prompt_data"]["indices_into_review_text"]
                            else:
                                indices_into_review_text = []
                            if "review_rating" in d["prompt_data"]:
                                review_rating = d["prompt_data"]["review_rating"]
                            else:
                                review_rating = -1 # -1 means unknown.
                            if "review_id" in d["prompt_data"]:
                                review_id = d["prompt_data"]["review_id"]
                            else:
                                review_id = ""
                            if "prompt_sentence" in d["prompt_data"]:
                                prompt_sentence = d["prompt_data"]["prompt_sentence"]
                            else:
                                prompt_sentence = ""                  
                            prompt_data = {
                                "indices_into_review_text": indices_into_review_text,
                                "review_rating": review_rating,
                                "prompt_sentence": prompt_sentence,
                                "review_id": review_id,
                            }
                        else:
                            prompt_data = {
                                "indices_into_review_text": [],
                                "review_rating": -1, # -1 means unknown.
                                "prompt_sentence": "",
                                "review_id": "",
                            }
                        yield d["text_id"], {
                            "id": d["text_id"],
                            # DynaSent Example.
                            "hit_ids": d["hit_ids"],
                            "sentence": d["sentence"],
                            "sentence_author": d["sentence_author"],
                            "has_prompt": d["has_prompt"],
                            "prompt_data": prompt_data,
                            "model_1_label": d["model_1_label"],
                            "model_1_probs": d["model_1_probs"],
                            "text_id": d["text_id"],
                            "label_distribution": d["label_distribution"],
                            "gold_label": d["gold_label"],
                            # Metadata.
                            "metadata": {
                                "split": split,
                                "round": round,
                                "subset": subset,
                                "model_in_the_loop": model_in_the_loop
                            }
                        }